Can you upload a human mind into a computer? A neuroscientist ponders what’s possible

The human brain has 86 billion neurons that make trillions of connections. Grafissimo/DigitalVision Vectors via Getty Images


Curious Kids is a series for children of all ages. If you have a question you’d like an expert to answer, send it to CuriousKidsUS@theconversation.com.

Is it possible to upload the consciousness of your mind into a computer? – Amreen, age 15, New Delhi, India

The concept, cool yet maybe a little creepy, is known as mind uploading. Think of it as a way to create a copy of your brain, a transmission of your mind and consciousness into a computer. There you would live digitally, perhaps forever. You’d have an awareness of yourself, you’d retain your memories and still feel like you. But you wouldn’t have a body.

Within that simulated environment, you could do anything you do in real life – eating, driving a car, playing sports. You could also do things impossible in the real world, like walking through walls, flying like a bird or traveling to other planets. The only limit is what science can realistically simulate.

Doable? Theoretically, mind uploading should be possible. Still, you may wonder how it could happen. After all, researchers have barely begun to understand the brain.

Yet science has a track record of turning theoretical possibilities into reality. Just because a concept seems terribly, unimaginably difficult doesn’t mean it’s impossible. Consider that science took humankind to the Moon, sequenced the human genome and eradicated smallpox. Those things too were once considered unlikely.

As a brain scientist who studies perception, I fully expect mind uploading to one day be a reality. But as of today, we’re nowhere close.
Living in a laptop

The brain is often regarded as the most complex object in the known universe. Replicating all that complexity will be extraordinarily difficult.

One requirement: The uploaded brain needs the same inputs it always had. In other words, the external world must be available to it. Even cloistered inside a computer, you would still need a simulation of your senses, a reproduction of the ability to see, hear, smell, touch, feel – as well as move, blink, detect your heart rate, set your circadian rhythm and do thousands of other things.

But why is that? Couldn’t you just exist in a pure mental bubble, inside the computer without sensory input?

Depriving people of their senses, like putting them in total darkness, or in a room without sound, is known as sensory deprivation, and it’s regarded as a form of torture. People who have trouble sensing their bodily signals – thirst, hunger, pain, an itch – often have mental health challenges.

That’s why for mind uploading to work, the simulation of your senses and the digital environment you’re in must be exceptionally accurate. Even minor distortions could have serious mental consequences.

For now, researchers don’t have the computing power, much less the scientific knowledge, to perform such simulations.

New and updated scanning technology is a necessity.
Scanning billions of pinheads

The first task for a successful mind upload: Scanning, then mapping the complete 3D structure of the human brain. This requires the equivalent of an extraordinarily sophisticated MRI machine that could detail the brain in an advanced way. At the moment, scientists are only at the very early stages of brain mapping – which includes the entire brain of a fly and tiny portions of a mouse brain.

In a few decades, a complete map of the human brain may be possible. Yet even capturing the identities of all 86 billion neurons, all smaller than a pinhead, plus their trillions of connections, still isn’t enough. Uploading this information by itself into a computer won’t accomplish much. That’s because each neuron constantly adjusts its functioning, and that has to be modeled, too.

It’s hard to know how many levels down researchers must go to make the simulated brain work. Is it enough to stop at the molecular level? Right now, no one knows.

Technological immortality comes with significant ethical concerns.
2045? 2145? Or later?

Knowing how the brain computes things might provide a shortcut. That would let researchers simulate only the essential parts of the brain, and not all biological idiosyncrasies. It’s easier to manufacture a new car knowing how a car works, compared to attempting to scan and replicate an existing car without any knowledge of its inner workings.

However, this approach requires that scientists figure out how the brain creates thoughts – how collections of thousands to millions of neurons come together to perform the computations that make the human mind come alive. It’s hard to express how very far we are from this.

Here’s another way: Replace the 86 billion real neurons with artificial ones, one at a time. That approach would make mind uploading much easier. Right now, though, scientists can’t replace even a single real neuron with an artificial one.

But keep in mind the pace of technology is accelerating exponentially. It’s reasonable to expect spectacular improvements in computing power and artificial intelligence in the coming decades.

One other thing is certain: Mind uploading will certainly have no problem finding funding. Many billionaires appear glad to part with lots of their money for a shot at living forever.

Although the challenges are enormous and the path forward uncertain, I believe that one day, mind uploading will be a reality. The most optimistic forecasts pinpoint the year 2045, only 20 years from now. Others say the end of this century.

But in my mind, both of these predictions are probably too optimistic. I would be shocked if mind uploading works in the next 100 years. But it might happen in 200 – which means the first person to live forever could be born in your lifetime.

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Quantum computers are coming to break our codes faster than anyone expected

Craig Costello, Queensland University of Technology

Online data is generally pretty secure. Assuming everyone is careful with passwords and other protections, you can think of it as being locked in a vault so strong that even all the world’s supercomputers, working together for 10,000 years, could not crack it.

But last month, Google and others released results suggesting a new kind of computer – a quantum computer – might be able to open the vault with significantly less resources than previously thought.

The changes are coming on two fronts. On one, tech giants such as IBM and Google are racing to build ever-larger quantum computers: IBM hopes to achieve a genuine advantage over classical computers in some special cases this year, and an even more powerful “fault-tolerant” system by 2029.

On the other front, theorists are refining quantum algorithms: recent work shows the resources needed to break today’s cryptography may be far lower than earlier estimates.

The net result? The day quantum computers can break widely used cryptography – portentously dubbed “Q Day” – may be approaching faster than expected.

The quantum hardware race

Quantum computers are built from quantum bits, or qubits, which use the counterintuitive properties of very tiny objects to carry out computations in a different and sometimes far more efficient way from traditional computers.

So far the technology is in its infancy, with the major goal to increase the number of qubits that can be connected to work as a single computer. Bigger quantum computers should be much better at some things than their traditional counterparts – they will have a “quantum advantage”.

Late last year, IBM unveiled a 120-qubit chip which it hopes will demonstrate a quantum advantage for some tasks.

Google also recently announced it planned to speed up its move to adopt encryption techniques that should be safe against quantum computers, known as post-quantum cryptography.

Alongside these tech giants, newer approaches are also flourishing. PsiQuantum is using light-based qubits and traditional chip-manufacturing technology. Experimental platforms such as neutral-atom systems have demonstrated control over thousands of qubits in laboratory settings.

In response, standards bodies and national agencies are setting increasingly concrete timelines for moving away from common encryption systems that are vulnerable to quantum attack.

In the United States, the National Institute of Standards and Technology (NIST) has proposed a transition away from quantum-vulnerable cryptography, with migration largely completed by 2035. In Australia, the Australian Signals Directorate has issued similar guidance, urging organisations to begin planning immediately and transition to post-quantum cryptography by 2030.

Algorithms make the lock-picking faster

Hardware is only half the story. Equally important are advances in quantum algorithms – ways to use quantum computers to attack encryption.

Much interest in quantum computer development was spurred by Peter Shor’s 1994 discovery of an algorithm that showed how quantum computers could efficiently find the prime factors of very large numbers. This mathematical trick is precisely what you need to break the common RSA encryption method.

For decades, it was believed a quantum computer would need millions of physical qubits to pose a threat to real-world encryption. This is far bigger than current systems, so the threat felt comfortably distant.

That picture is now changing.

In March 2026, Google’s Quantum AI team released a detailed study showing that far fewer resources may be needed to attack a different kind of encryption which uses mathematical objects called elliptic curves. This is what systems including Bitcoin and Ethereum use – and the study shows how a quantum computer with fewer than half a million physical qubits may be able to crack it in minutes.

That’s still a long way beyond current quantum computers, but around ten times less than earlier estimates.

At the same time, a March 2026 preprint from a Caltech–Berkeley–Oratomic collaboration explores what might be possible using neutral-atom quantum computers. The researchers estimate that Shor’s algorithm could be implemented with as few as 10,000–20,000 atomic qubits. In one design they propose, a system with around 26,000 qubits could crack Bitcoin’s encryption in a few days, while tougher problems like the RSA method with a 2048-bit key would need more time and resources.

In plain terms: the codebreakers are becoming more efficient. Advances in algorithms and design are steadily lowering the bar for quantum attacks, even before large-scale hardware exists.

What now?

So what does this mean in practice?

First, there is no immediate catastrophe – today’s cryptography won’t be broken overnight. But the direction of travel is clear. Each improvement in hardware or algorithms reduces the gap between current capabilities and useful quantum cracking machines.

Second, viable defences already exist. NIST has standardised several post-quantum cryptographic algorithms which are believed to be resistant to quantum attacks.

Technology companies have begun deploying these in hybrid modes: Google Chrome and Cloudflare, for example, already support post-quantum protections in some protocols and services.

Systems that rely heavily on elliptic-curve cryptography – including cryptocurrencies and many secure communication protocols – will need particular attention. Google’s recent work explicitly highlights the need to migrate blockchain systems to post-quantum schemes.

Finally, this is a two-front race. It is not enough to track progress in quantum hardware alone. Advances in algorithms and error correction can be just as important, and recent results show these improvements can significantly reduce the estimated cost of attacks.

Every new headline about reduced qubit counts or faster quantum algorithms should be understood for what it is: another step toward a future where today’s cryptographic assumptions no longer hold.

The only reliable defence is to move – deliberately but decisively – toward quantum-safe cryptography.The Conversation

Craig Costello, Professor, School of Computer Science, Queensland University of Technology

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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